Thomas Vavra | New Ways of Handling Old Data

New Ways of Handling Old Data
Tom Vavra
AVP Software & Industry Insights and Analysis
IDC

Top Trends
© IDC Visit us at IDC.com and follow us on Twitter: @IDC 2
People Centric Networks
Intersection of People to People to Data
Cognitive Computing & Assistive Technology
Work Context & flow
Organizational Dynamics
Sales, marketing, service not “working”
Shifting workforce dynamics (for the 1st time
Millennials are the largest % of the workforce)
Disruptive new connected business models
Applications are Changing
Social: Inherent Ability to Connect
Underlying Platform Services
Distributed Information Access – Decision
Support

Unstructured Content:
Value Waiting to be Delivered
Unstructured content –
email, video, instant
messages, documents,
and other formats –
accounts for
of all digital information
Unlocking value from
this content should be
the goal of every
organization, but very
few are actually
getting all the value
they should be.
THIS CONTENT IS LOCKED IN A VARIETY LOCATIONS AND APPLICATIONS MADE UP
OF SEPARATE REPOSITORIES THAT DON’T TALK TO EACH OTHER – E.G., EMC
DOCUMENTUM, SALESFORCE.COM, GOOGLE DRIVE, SHAREPOINT, ET AL.
90%

IDC’s Big Data and Analytics
Predictions (1)
1. Through 2020, spending on cloud-based BDA technology will grow 4.5x
faster than spending for on-premises solutions; open source technology will
represent the core of this new architecture.
2. By 2020, 50% of all business analytics software will incorporate prescriptive
analytics built on cognitive computing functionality.
3. Shortage of skilled staff will persist and extend from data scientists to
architects and experts in data management; big data–related professional
services will have a 23% CAGR by 2020.
4. By 2020, 90% of databases (relational and non-relational) will be based on
memory-optimized technology.
5. By 2020, distributed micro analytics and data manipulation will be part of all
big data and analytics deployments.

IDC’s Big Data and Analytics
Predictions (2)
5. Through 2020, spending on self-service visual discovery and data
preparation market will grow 2.5x faster than traditional IT-controlled tools
for similar functionality.
6. By 2020, data monetization efforts will result in enterprises pursuing digital
transformation initiatives increasing the marketplace’s consumption of their
own data by 100-fold or more.
7. By 2020, the high-value data part of the digital universe that is worth
analyzing to achieve actionable intelligence will double.
8. By 2020, 60% of information delivered to decision makers will be
considered by them always actionable, doubling the current rate.
9. By 2020, organizations able to analyze all relevant data and deliver
actionable information will achieve an extra $430 billion in productivity
benefits over their less analytically oriented peers.

Why Do So Few Organizations
Find Value in Their Information?
of knowledge workers
regularly access 4 or
more systems to get
the information they
need to do their jobs
of a typical knowledge
worker’s day is spent looking
for and consolidation
information spread across a
variety of systems
61% 36%
Nearly 15% access
11 or more systems
These workers find the information required
to do their jobs only 56% of the time

Cognitive Software Attributes
• Performs deep natural language processing and analysis both for
information ingestion and research as well as to provide human style
communication (usually posed as questions and answers)
• Conducts learning in real time as data arrives
• Has the ability to identify similar past experiences and use learning to
current situation
• Predicts and recommends possible outcomes
• Score those outcomes with evidence for human analysis
• Cycle back to the start so that the continuous learning is practiced, making
the system better over time
Cognitive software support human decision-making with more accuracy,
confidence, speed, and agility based on broader and deeper bodies of evidence
applied to a more comprehensive view of pertinent conditions without bias.

The Content Analytics, Discovery &
Cognitive Systems Market Defined
 Content Analytics
• Text Analytics, Video Analytics
• Categorizers and clustering engines
• Speech Recognition, Language analyzers
 Discovery
• Enterprise search engines, information
access platforms, and applications for
browsing and navigation
• Knowledge Base/Graph Generation
• Rich media search
 Cognitive Systems
• Digital assistants
• Automated advisors
• Artificial intelligence, deep learning and
machine learning
• Automated recommendation systems
CADCS software analyzes, organizes, accesses, and provides advisory
services based on a range of unstructured information and provides a platform
for the development of analytic and cognitive applications.

9
Cognitive Solutions Ecosystem
Source: IDC
Behavioral Interactional
Performance
Long form
Geolocation
News
Personal data
Healthcare
Location
Sports &
Entertainment
Social
Corporate
Logistics
Financial
Marketing
Sales
Procurement
Asset
mgmt.
R&D
Logistics
HR
Anti money
laundering
Retail
pricing
Patient
outcomes
Telco
churn
IT performance
mgt.
Retail
Travel
Media
Healthcare
Insurance
Investment
Commercial
leasing
Advertising
Legal
Driverless
cars
Smart home
devices
Self-flying
drones
Robotic
systems
Text analysis
Video analysis
Image analysis
Predictive analytics
NLP
APIs
ConnectorsData stores
Hypotheses generation
Machine learning
Speech Recognition
Dialogue Mgt.
Finance
Risk mgmt.
Weather

Cognitive Systems Use Cases
 Healthcare
• Diagnosis and Treatment Systems
• Education and Training Systems
• Pharmaceutical Research and Discovery
 Retail
• Expert Shopping Advisors & Product Recommendations
• Automated Customer Service Agents
• Automated Training Systems
 Finance/Insurance
• Automated Financial Advisors
• Policy Advisors & Question and Answer Systems
• M&A Investigation and Recommendations
 Government
• Police Investigation Systems
• Program Advisors and Recommendation Systems
 Manufacturing
• Operational Improvement Systems
• Asset Maintenance Systems
10

11
Source: IDC, 2015
2014–2019 Revenue ($M) with Growth (%)
($M) (%)
827 1075
1419
1916
2644
3683
0
5
10
15
20
25
30
35
40
45
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
2014 2015 2016 2017 2018 2019
Cognitive Total growth (%)
Game Changer
Commercial cognitive software platforms have just begun to emerge on
the market scene. This category of software used to build “smart”
applications and expert advisors will grow rapidly over the next five years
enabling a multi-billion dollar intelligent applications market.
2014–2019 Revenue ($M) with Growth (%)
($M) (%)
Game Changer
Commercial cognitive software platforms have just begun to emerge on
the market scene. This category of software used to build “smart”
applications and expert advisors will grow rapidly over the next five years
enabling a multi-billion dollar intelligent applications market.
Worldwide Cognitive Software Platform
Forecast

Worldwide Cognitive Market by Industry, 2015

WW Cognitive Systems Spending
(US$M) by Use Case, 2015
$- $ 100 $ 200 $ 300 $ 400 $ 500 $ 600 $ 700
Adaptive Learning
Asset/Fleet Management
Automated Claims Processing
Automated Customer Service Agents
Automated Threat Intelligence and Prevention Systems
Defense, Terrorism, Investigation and Government Intelligence Systems
Diagnosis and Treatment Systems
Expert Shopping Advisors & Product Recommendations
Fraud Analysis and Investigation
Freight Management
Merchandising for Omni Channel Operations
Others
Pharmaceutical Research and Discovery
Program Advisors and Recommendation Systems
Public Safety and Emergency Response
Quality Management Investigation and Recommendation Systems
Regulatory Intelligence
Sales Process Recommendation and Automation
Value (USD M)

European Cognitive Systems Spending
(US$M) by Use Case, 2015
$- $ 20 $ 40 $ 60 $ 80 $ 100 $ 120
Adaptive Learning
Asset/Fleet Management
Automated Claims Processing
Automated Customer Service Agents
Automated Threat Intelligence and Prevention Systems
Defense, Terrorism, Investigation and Government Intelligence Systems
Diagnosis and Treatment Systems
Expert Shopping Advisors & Product Recommendations
Fraud Analysis and Investigation
Freight Management
Merchandising for Omni Channel Operations
Others
Pharmaceutical Research and Discovery
Program Advisors and Recommendation Systems
Public Safety and Emergency Response
Quality Management Investigation and Recommendation Systems
Regulatory Intelligence
Sales Process Recommendation and Automation
Supply and Logistics
2015 Value (USD M)
2
3
9
11
12
1

Source: IDC, 2016
Practices to Implement Cognitive
Systems Initiatives

Source: IDC, 2016
1 - Setting Expectations

Be Realistic
Issues
 Business and IT both assume Cognitive will
replace humans
 Cognitive can only assist
 Outputs are never a “sure bet”
 Requires collaboration between IT and LOBs
 Relevant data is needed, not just more of it

Real World #1: Bankers vs. Robots

Source: IDC, 2016
2 – Leverage Cloud Services

Cloud as a Facilitator and Problem
Solver
Issues
 Cognitive systems require vast data and
processing power
 On premise investment can be expensive and
time consuming
 Cloud services can do “heavy lifting” and
alleviate up front costs and time…
 … but, not all Cognitive solutions are Cloud-
ready or appropriate

Real World #2 :When Planes Love
Clouds
We Cloud

Source: IDC, 2016
3 – Identify Repetitive Routine Actions

Choosing a Starting Point
Issues
 Identifying the right use case takes time and
thought
 Need to start by documenting current business
processes to identify resource-intensive tasks
 Narrow down list for cognitive applications
 Free up human resources to analyze exceptions
and outliers

Real World #3 : Letting Doctors be
Doctors

Source: IDC, 2016
4 – Validate Outputs

Cognitive systems don’t fix bad inputs
and untrained users
Issues
 The lack of quality inputs and expert trained
users will, necessarily, result in mistakes and
bad outputs
 Surprises are common in the early stages
 Constant validation is required to minimize
erroneous results
 Feedback on errors must be part of the regular
workflow

Real World #4 : Matching Clients with
Hotels

Source: IDC, 2016
5 – Manage Data to Avoid Inaccurate
Results

Data management is always central
and key to any initiative
Issues
 Data can have many issues: inconsistency,
varied formats, ownership, (lack of) governance
 Need to map data: Where is it? Who owns it? Is
it connected/integrated?
 Third-part data is often required to complement
existing sources
 Value of data increases exponentially when
different types and sources are combined

Real World #5 : IT Mfg + Internal IT +
External provider

Thank you!
Tom Vavra
Tel: + 420 221 423 140
tvavra@idc.com
Associate Vice President
IDC CEMA
Malé naměstí 13
110 00 Praha 1
Czech Republic
www.idc-cema.com
www.idc.com
CEMA Region

Thomas Vavra | New Ways of Handling Old Data

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